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ERASMUS UNIVERSITEIT ROTTERDAM Master Thesis
ROTTERDAM SCHOOL OF MANAGEMENT
A DATA-DRIVEN APPROACH TO PLUG-IN ELECTRIC VEHICLES
CHARGING BEHAVIOUR
AUTHOR:
ALEXANDER
NICOLAIJ
381143
THESIS COACH:
YASHAR GHIASSI-FARROKHFAL
CO-READER:
DERCK KOOLEN
BIM Master thesis trajectory 2016-2017
DATE OF SUBMISSION -
BILL-M BY
MEDIAMARKT
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PREFACE
The copyright of this master thesis proposal rests with the author. The author is responsible
for its contents. RSM is only responsible for the educational coaching and cannot be held
liable for its content.
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EXECUTIVE SUMMARY
It can be seen that electric transport (ET) has grown rapidly in the past years. ET is as an
important link in the transition to a sustainable energy supply. Electric transport provides
promising developments with regard to sustainability problems such as CO2-emission, air
pollution and rising fossil fuel prices. Electric vehicles (EV) have a small carbon footprint,
have less impact on air quality and are cheaper to drive than conventional cars. However,
there are challenges ahead for a broad implementation of EVs in the Netherlands such as
electricity peak demand problems and charging point availability. The EV user is with his
charging behaviour an important variable in a well-functioning charging system. This
research aims at understanding how charging behaviour looks like and what factors
constitute this behaviour by using a data-driven approach. The charging behaviour and
demand of EV users has to be understood in order to optimize and evenly distribute the
utilization rate. This research greatly benefit with the intention to get a better understanding
of when and how EV drivers use the public charging infrastructure. The research is based on
a database from EVnetNL, the managing company of over 3000 public charging points in the
Netherlands (EVnetNL, 2017). The structure is as follows, first a literature review was
performed to conceptualize what the factors are which constitute charging behaviour. These
are time, location, infrastructure, charging duration and charge consumption. Looking at the
results, EV drivers show similar charging profiles in which strong peaks are noticeable at
times on which EV drivers start and stop charging transactions at the same moment. Also,
the majority of charging transactions last much longer than required, indicating inefficient
use of charging points. In addition, many EV drivers charge based on routine, with clear
charging start and stopping peaks. To minimize these problems, charging station operators
could use the potential of smart charging technology to make EV charging behaviour more
efficient. Many EV drivers connect for long periods during the night and during work times.
This gives the opportunity to electricity producers to influence and control the charging
procedure, to reduce the energy demand problem and still make sure that the battery of the
EV is fully charged. Furthermore, smart charging technology could also develop the
potentials that are there in current Dutch EV charging behaviour such as long charging
durations during the night and during work-hours.
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TABLE OF CONTENTS
PREFACE ...................................................................................................................................... 2
EXECUTIVE SUMMARY .................................................................................................................. 3
1 | INTRODUCTION ....................................................................................................................... 6
1.1 RESEARCH CONTEXT ................................................................................................................ 8
1.2 RESEARCH GOAL ...................................................................................................................... 9
1.3 PRACTICAL AND ACADEMICAL RELEVANCE ............................................................................. 9
2 | LITERATURE REVIEW ............................................................................................................. 11
2.1 RESEARCH IN E-MOBILITY ...................................................................................................... 11
2.2 TIME ....................................................................................................................................... 12
2.3 LOCATION .............................................................................................................................. 13
2.4 INFRASTRUCTURE .................................................................................................................. 15
2.5 CHARGING DURATION ........................................................................................................... 16
2.6 CHARGING FREQUENCY ......................................................................................................... 17
2.7 CHARGE CONSUMPTION ....................................................................................................... 18
3 | METHODOLOGY & DATA ANALYSIS ........................................................................................ 20
3.1 MODEL SETUP ........................................................................................................................ 20
3.1.2 DATA DESCRIPTION & PREPARATION ................................................................................ 21
4 | RESULTS ................................................................................................................................ 25
4.1 TIME ....................................................................................................................................... 25
4.2 CHARGING DURATION ........................................................................................................... 29
4.3 CHARGING FREQUENCY ................................................................................................ 31
4.4 CHARGE CONSUMPTION ....................................................................................................... 33
4.5 INFRASTRUCTURE .................................................................................................................. 35
4.6 LOCATION .............................................................................................................................. 36
4.7 RELATIONSHIPS BETWEEN DIMENSIONS .............................................................................. 42
5 | CONCLUSION ........................................................................................................................ 44
5.1 THEORETICAL CONTRIBUTION .............................................................................................. 44
5.2 MANAGERIAL IMPLICATIONS ................................................................................................ 46
5.3 LIMITATIONS AND FUTURE WORK ........................................................................................ 46
6 | BIBLIOGRAPHY ...................................................................................................................... 48
7 | APPENDIX ............................................................................................................................. 50
7.1 LIST OF FIGURES .................................................................................................................... 50
7.2 LIST OF TABLES ...................................................................................................................... 50
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7.3 R CODE ................................................................................................................................... 51
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1 | INTRODUCTION With the global warming effects, rising prices of oil and the depletion of natural resources,
the debate on alternative energy sources is getting more pressing and more people around
the world are getting involved. By transitioning to sustainable technologies, such as solar
and wind power, it is possible to achieve energy independence and stabilize human-induced
climate change.
Figure 1: Emission of greenhouse gasses in the Netherlands (CBS, 2016)
Increasing transportation efficiency is the best place to start efforts to reduce emissions of
carbon dioxide (CO2), which is a primary issue in global warming. Of all CO2 emissions in the
Netherlands, about 29 billion kg CO2-equivalents comes from transportation, that is
respectively 15% of the total of CO2-equivalents in the Netherlands (CBS, 2016).
It can be seen that electric transport (ET) has grown rapidly in the past years. ET is as an
important link in the transition to a sustainable energy supply. Electric transport provides
promising developments with regard to sustainability problems such as CO2-emission, air
pollution and rising fossil fuel prices. Electric vehicles have a small carbon footprint, have
less impact on air quality and are cheaper to drive than conventional cars.
The adoption of plug-in electric vehicles in the Netherlands is actively supported by the
Dutch government (RVO, 2017). Considering the potential of plug-in electric vehicles in the
country due to its relative small size and geography, the Dutch government set a target of
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200,000 electric vehicles with three or more wheels on the roads in 2020; and 1 million
vehicles in 2025. A growing number of Dutch consumers consider buying an electric vehicle
and sales of domestic electric vehicles in the Netherlands have been rising from 1100 in
December 2011 to more than 115.223 in December 2016 (RVO, 2017). However, there are
challenges ahead for a broad implementation of EVs in the Netherlands. In 2015, only 9.7%
of all vehicle registrations was a completely electric (BEV) or plug-in hybrid vehicle (PHEV). In
2016, this number declined to 6.4% (Special: Analyse over 2016, 2017). One of the biggest
concerns, negatively impacting EV adoption, is range anxiety (Neubauer, 2014).
Furthermore, charging infrastructure is also a vital factor that impacts a country’s EV
adoption rate.
The introduction of e-mobility vehicles is positively correlated to the investments in
infrastructure. This is especially the case for BEVs because they their only source of power is
electricity and have a narrow range limit. Therefore, it is critical to invest in public and semi-
public charging stations. There are large parts of the Netherlands where there are little or no
public charging stations available. In the larger cities, it is not a problem, but the parking
space needed for expansion of the charging network is limited and most people have no
ability to recharge at home. Seventy percent of the Dutch with a car does not have a
driveway (greendeals, 2016). Furthermore, the distribution of the number of publicly
accessible charging points in Netherlands is unevenly distributed. If the number of electric
vehicles continues to grow as predicted, there is a large deficit in accessible charging points
over a number of years.
As of January 2017, there are 11.721 public charging points, 14.930 semi-public points and
614 fast charging points in the Netherlands (RVO, 2017). This research will focus on the
charging behaviour of plug-in electric vehicles that among others plays an important role in
the utilization rate of public infrastructure charging points.
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1.1 RESEARCH CONTEXT
The electric vehicle (EV) user is with his charging behaviour an important parameter in a
fine-performing charging system. This research aims at understanding what this charging
behaviour looks like and what factors create this behaviour, which may help to develop
strategies for stimulating a more efficient utilization of the charging. In order to optimize,
evenly distribute the utilization rate and understand the charging demand, the charging
behaviour has to be understood.
This leads to the following research question:
What are the main factors that constitute plug-in electric vehicle charging behaviour
and how are they formed in the Netherlands?
In order to support this research question, the following sub questions are drawn up:
1. What are the main factors influencing charging behaviour according to literature?
2. How is charging behaviour formed with the use of these factors in the Netherlands?
In order to get a deep and rich understanding of which factors exactly constitute plug-in
electric vehicle charging behaviour the Netherlands, it is necessary to conduct a literature
review because it will shed light on the factors which are already discussed by scientists.
Furthermore, the research part contains quantitative analysis which gives a more in-depth
insight on the discovered factors from the literature review. This will consist of analysing raw
charging data to and patterns in the use of public charging infrastructure.
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1.2 RESEARCH GOAL
This paper aims to understand how electric vehicle charging behaviour is constituted in the
Netherlands. By taking external factors into account, valuable insight of how charging
behaviour is shaped will be identified.
The research goal can be described as follows:
To develop a better understanding of plug-In electric vehicle charging behaviour in the
Netherlands.
In order to achieve this research goal and to answer the research questions, this paper will
be performed with the use of a data set from EvnetNL, the managing company of over 3000
public charging points in the Netherlands (EVnetNL, 2017).
1.3 PRACTICAL AND ACADEMICAL RELEVANCE
As has been mentioned before, charging behaviour of EV drivers and the charging
infrastructure are current and popular items. Not many researches have been done by
scholars on the different factors that explain how charging behaviour is shaped in The
Netherlands. Therefore, this research will greatly benefit with the intention to get a better
understanding of when and how EV drivers use the public charging infrastructure.
Furthermore, the insight in charging behaviour in the public charging infrastructure
environment in this research will contribute to the limited existing literature. Research in
this area has been done but is limited due the use of older data bases. Therefore, it is only
based on the charging behaviour of early adopters, who were still limited by technological
barriers. There are more than 25.000 public and semi-public charging points and many more
vehicles types have been released on the market nowadays. Thus, investigating the current
charging behaviour and infrastructure will be of a great value. And last, current literature on
charging behaviour varies enormously among scientific papers and misses a clear overview.
The literature review of this research will provide this overview of perspectives on charging
behaviour.
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On the more practical side, the insights from this paper will benefit both the EV driver as
well as the charging station providers. The managing providers can use these insights for
determining the location of new charging infrastructure. In addition, results this paper
develop make a contribute to clear out several EV related problems like improving
availability of charging infrastructure by informing managing providers. Furthermore, the
peak loads on the energy grid can be avoided and smooth out by giving information of the
distribution of EV transactions. Charging infrastructure that is reliable and works efficiently is
crucial for the transition towards a more sustainable system.
1.4 STRUCTURE
The thesis starts with the literature review which answers the first sub research question. It
focuses on the various dimensions discovered in literature about charging behaviour and the
use of charging infrastructure. This is necessary in order to get a better understanding in the
important concepts. The next chapter describes the methodology of the thesis, including the
methods being used and the data analysis. After the methodology and data analysis, in
chapter three, an overview of the main results according to each dimension from the
literature review will be described which answers the second sub research question. In the
end of this thesis, the results and conclusion are described. This conclusion gives a deep and
enriched answer on the main research question and, in addition, reflects on limitations and
future research.
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2 | LITERATURE REVIEW
The main focus of the research is on determining the main factors that constitute plug-in
electric vehicle charging behaviour . This chapter will cover the literature review which gives
an overview in the different perspectives of charging behaviour. Relevant concepts of PEV’s,
their charging services and charging behaviour will be discussed. It is important to get a high
level view to provide background and sufficient support for the research questions
formulated.
2.1 RESEARCH IN E-MOBILITY
Studies in e-mobility, especially electric vehicles, have been getting more and more attention
in the recent years. The first papers about e-mobility found their way in the 1990’s, however,
most of them investigated only the market since infrastructure and development of EV’s
were not ready for mass-adoption (Franke & Krems, 2013). With the sale of the Tesla
Roadster in 2008, the discussions around PEV usability have risen again. However, many
things changed due to the complexity of the subject and thus involved many new
stakeholders. A broad number of studies have been written on the topics of PEV user
charging and driving behaviour, the economics of charging stations, load prediction and the
overall infrastructure requirements for the mass deployment and adoption of PEV’s.
However, many areas remain still unexplored (Kley, Dallinger, & Wietschel, 2010). To provide
background and sufficient support for the research questions formulated, different articles
will be discussed that describe a wide range of dimensions that influence the charging
behaviour of EV drivers. Charging behaviour could be seen as the term for all interactions
between an EV user and an EV charging point in the context of EV’s. Multiple studies have
been conducted taken charging behaviour into account. Therefore, definitions and
descriptions differ tremendously along several studies in literature. It is necessary to
evaluate the dimensions of charging behaviour of EV drivers to get a clear overview what
constitutes the plug-in electric vehicles charging behaviour in the Netherlands. The literature
review will start with a chapter with different dimensions. These include time, location,
infrastructure, charging duration, charging frequency and charge duration. Besides looking at
the charging behaviour at public charging points it is important to also take home and work
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charging points into account to get an adequate understanding on this topic. For the
literature review the scientific online search engine Sciencedirect
(http://www.sciencedirect.com) was used, which contains peer-reviewed scientific literature
only.
2.2 TIME
Charging time of day concerns at what time in the day the charging transaction takes place.
According to the study of (Weiller, 2011): a morning charging peak occurs at work and the
evening peak occurs mainly as drivers arrive home. Research by Smith et al. (2011) on
battery size optimization shows that the majority of EV’s is parked at home from 21:00 until
7:00. Also, the average commuter EV is parked at work from 09:00 until around 15:00.
(Smith, Shahidinejad, Blair, & Bibeau, 2012). The study by (Kelly, Macdonald, & Keoleian,
2012) on the behaviour of time-dependent PHEV vehicle charging adds that on average the
EV charging load on the electricity grid starts to rise significantly around 16:00, followed by a
charging peak at 21:00, and that the charging load on the grid usually ends around 04:00,
proving that EV owners generally charge during the night. Furthermore, because of people’s
different patterns of activities on the weekends, PHEV electric load is distributed differently
than during weekdays.
Research by (Weiller, 2011) on PHEV impacts on hourly electricity demand shows that there
are two load profiles, with charging possible at home, work and commercial places at
intermediate-level power. Interestingly, although week-ends are off-peak periods, the peak
power from PHEV charging is higher on weekends than on weekdays when charging is
enabled anywhere. PHEV load is distributed very differently over the day, with a single peak
occurring around 13:00, in contrast to week- days that have morning and evening peaks. The
mid-day peak on weekend days would occur mainly in commercial areas (strip malls,
restaurants, shopping centers. A small portion of the load in the morning is due to charging
at the workplace. Power demand in commercial centres have a much higher peak on
weekends than on weekdays. With regard to both electricity peak problems and
infrastructure availability, a more spread pattern of charging demand is preferred.
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Electric companies are concerned about the effects of introducing EVs into the grid,
especially with a large amount (Chicharro, Latorre, & Ramos, 2014). The charging pattern of
EVs is the main factor that determines these effects. Unregulated charging (probably when
returning home) means that large amounts of electricity are demanded within a small
amount of time which would have undesirable consequences for the energy providers.
Moreover, this implies that a considerable number of EV drivers simultaneously prefer to
charge at a given point in the day, which leaves the availability of charging points to be
inefficient and insufficient (Benysek & Jarnut, 2012). Results of (Weiller, 2011) show that the
commercial roll-out of PHEVs will not necessarily require complex optimization algorithms or
investments in ‘‘smart charging’’ controls but that a delayed charging option could be
sufficient to avoid the evening peak load. Reason being is that electricity demand is usually
low during the night and there is sufficient time to charge the EV.
2.3 LOCATION
The charging point location dimension concerns the type of location where a vehicle is
charged.
Research by (Graham-Rowe, et al., 2012) on EV drivers in the UK indicate that charging at
home was valued by some because it promoted autonomy . Time spent waiting for the car to
charge was commonly viewed as ‘dead time’, and waiting was seen to compromise freedom
of movement, so negating a highly-valued affective benefit of driving when charging at a
public point or at work. The study of (Skippon & Garwood, 2011) confirms this view that EV
drivers with access to a drive prefer to charge at home overnight.
The following most popular class of location for recharging is workplace car parks. For
charging at work, the convenient location, less effort and convenient timing makes it
different from charging at public stations (Jabeen, Olaru, Smith, Braun, & Speidel, 2013).
Drivers having travel commitments involving other family members show a stronger
preference for charging EV at public stations according to (Jabeen, Olaru, Smith, Braun, &
Speidel, 2013). This could be due to the requirement for their long trip, involving a
pickup/drop of a family member or some household chores. Thus, EV drivers need specific
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reasons to charge at work or at a public stations. Of public charge point locations, car park
locations were favoured by EV users (Morrissey, Weldon, & O'Mahony, 2016). Furthermore,
the load profiles for urban and rural household vehicles display similar shapes with morning,
mid-day and evening peaks. The study of (Weiller, 2011) specifies that Rural load is
consistently higher than urban load, especially during the peak period of the day (08:00-
19:00) when it is between 25% and 33% higher. This is not surprising since urban drivers
typically cover fewer kilometres per day and public transportation takes on a share of the
transportation dominated by private vehicles in rural areas. According to the research of
(Morrissey, Weldon, & O'Mahony, 2016) public charge point locations and car park locations
were favoured by EV users. The research of (Morrissey, Weldon, & O'Mahony, 2016) states
that the peak in evening charging demand for household charge points is likely a result of
the preference of EV users to plug in their vehicles immediately following after the
culmination of a working day.
Concerning the problem of energy peaks on the electrical grid, it is preferred by EV drivers to
use public or work charging points apart from home charging points (Chicharro, Latorre, &
Ramos, 2014). Because of this, the charging transaction might be more spread out which
reduces peak problems.
In addition, charging point density relates to the amount and coverage of charging points in
the surroundings of EV’s. The study of (Kelly, Macdonald, & Keoleian, 2012) state in their
study that a high charging point density reduces the need for planning and lowers the range
anxiety of EV drivers. Reason being that there is always a charge point close by.
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2.4 INFRASTRUCTURE
Infrastructure relates to all the services of charging EVs. There are two main types of
charging points used in this research: standard chargers (delivering a power equal or lower
than 22 kW) and fast chargers (delivering power above 22 kW). (Dong, Liu, & Lin) state that
more public chargers, when optimally located, could effectively reduce range-constrained
days and trips for BEV drivers. (Kley, Dallinger, & Wietschel, 2010), after examining demand
patterns, claimed that wired, private charging stations with low power connections are
adequate to convert over 50% of all current combustion engine vehicles into PEV’s, based on
mobility behaviour. Nevertheless, due to range anxiety, and other factors aiming to make
PEV’s more attractive to a wider audience, fast chargers remain relevant (Schroeder &
Traber, 2012). Fast charging technology has the benefit of assisting long-range drives for
electric vehicles, and thus could serve as a means to mitigate range anxiety as stated in
(Schroeder & Traber, 2012), with EV users having the opportunity to access public charging
infrastructure at times and places where they are running low on charge. This factor may be
crucial helping push market penetration of EVs toward the goal of 1 million EV in 2025, as set
by the Dutch government. Fast charging attracts EV users for it replicates the ease of
conventional refuelling and it attracts potential operators for it promises interesting
business options. Possible disadvantages of this technology are fiercely discussed. These
include notably the impact on battery lifetime, electricity grid and renewable energy
integration. The thought that EV drivers tend to favour fast-charging points is acknowledged
by (Neubauer, Brooker, & Wood, 2013). However they add a warning that regular use of
these high-power charging points could negatively affect the EV battery life and depth of
discharge of the battery. The research of (Weiller, 2011) states that investments in charging
stations and infrastructure should primarily be directed at homes and offices, where PHEVs
are likely to be plugged-in for the longest time. According to the study of (Morrissey,
Weldon, & O'Mahony, 2016) standard charge points were found to be utilised to a greater
extent earlier in the day with fast charge points utilised more in the evening and night. It is
likely that the higher usages of public standard chargers earlier in the day coincide with EV
users commuting in the early morning period and then plugging in their vehicles at standard
chargers when they have more time available to allow for the charge to take place, and also
due to these types of chargers being the only ones available at their destinations.
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2.5 CHARGING DURATION
Charging duration involves the amount of time a charging transaction takes places. Charge
durations were found to be highest at household locations, which is to be assumed as it is
likely to be a user’s preferred location to undertake the majority of their charging as stated
by (Morrissey, Weldon, & O'Mahony, 2016). According to the research of (Graham-Rowe, et
al., 2012) and (Skippon & Garwood, 2011) charging was made difficult by lengthy charge
times. This seems to be the main disadvantage of EV’s identified by the drivers. Especially
compared to a 5 minute gasoline refill. EV drivers have to move schedules around to charge
their car up. In addition, according to the study of (Skippon & Garwood, 2011) 78% of EV
drivers would choose for minor capital expenditure of €350 to reduce at-home full
recharging time from 8 h to 3 h. However an equally significant majority, also 78% of EV
drivers, would choose against more substantial capital expenditure € 2300 to reduce at-
home full recharging time from 8 to 1 h. The study of (Hidrue, Parsons, Kempton, & Gardner,
2011) shows comparable results that EV drivers would pay for a decrease in charging
duration. The study shows that EV drivers are willing to pay from €400 to €3000 per hour
reduction in charging time (for a 80km charge). Range anxiety and long charging time remain
consumers’ main concerns about electric vehicles.
A long charging duration could provide benefits with respect to the electricity demand
problem. This is mainly because a charging transaction for a full battery does not take more
than 8 hours of charging for example. As a result, it opens the possibility to use smart-
charging technology as there is more flexibility within the charging transaction. This
technology could delay the transaction to a point where energy demand is at its lowest
(Chicharro, Latorre, & Ramos, 2014). On the other hand, long transactions are not efficient
regarding to the availability of charging points.
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2.6 CHARGING FREQUENCY
The frequency of EV charging transactions concerns how often an EV is charged. Research
by (Smart & Powell, 2013) about charging behaviour findings from a Chevrolet Volt project in
the US shows that over 80% of the Volt drivers charged their PHEV more than once per day,
on average. It also is apparent that considerable variation existed in charging frequency from
vehicle to vehicle. One vehicle averaged as high as 3.2 charging events per day, when the
vehicle was driven. Some vehicle owners did not seem as compelled to charge their vehicles;
the minimum vehicle average charging frequency was 0.14 times per day driven or once per
week. On average the Volt Drivers charged their PHEV 1.46 times a day.
Driver motivation to plug in his/her vehicle varies dramatically from driver to driver. Vehicles
with an average charging frequency above two charging events per day driven were found to
have more variation in their charging frequency from day to day. These vehicles
performed from 0 to 8 charging events in a 24-hour period. In the article of (Franke & Krems,
2013) about understanding the charging behaviour of EV users is charging frequency stated
in the concept of ‘User-Battery-Interaction (UBI)’ as developed by Rahmati and Zong (2009).
Each driver can be assessed by a different ‘UBI-score’. An EV driver with a low score means
that he or she is not actively interacting with the battery and charges the EV routinely (e.g.
daily when returning from work). A high UBI-score indicates that the EV driver is actively
checking the battery on whether how much it is charged and making charging-decisions
based on this information. Regarding sustainable interaction, a higher UBIS is presumed to
be beneficial because it is presumably related to a more efficient utilization of limited range
resources. Users with a higher UBIS more actively interact with battery resources and may
more often exhaust the available range. Yet, under conditions where a more frequent
connection to the power grid would be beneficial (i.e., wind to vehicle charging), a low UBIS
would be preferable from a sustainability perspective.
In addition, research of (Smith, Shahidinejad, Blair, & Bibeau, 2012) express that a high
charging frequency is favoured as the batteries of EVs could be smaller. This is only possible
if the charging point infrastructure is high in density and requires well balanced and well
planned mobility behaviour. Taken the energy peak problem into account, EV drivers prefer
to charge their EV based on battery level information as a replacement for routine behaviour
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(Franke & Krems, 2013). This is due the fact that routine behaviour is not flexible and based
on suitability, which makes it difficult in adapting the behaviour to make it possible to lower
inefficiencies. A high charging frequency could have a positive effect on the charging
infrastructure when average charge consumption is lower per transaction. This is because it
will decrease the load during peak demand periods. Furthermore, given the higher usage
frequencies recorded by the fast chargers in the research of (Morrissey, Weldon, &
O'Mahony, 2016), it is clear that this is an area of high demand within EV users' public
charging needs.
2.7 CHARGE CONSUMPTION
The charge consumption concerns the amount of energy that is added to the EV battery
during the charging transaction. The research of (Smart & Powell, 2013) tested among
others the charging completeness. For one quarter of the charging events, the battery of the
EV had been fully or nearly fully depleted by the time the drivers plugged in their vehicles.
The vast majority of home charging events resulted in a full or nearly full battery. In general,
away-from-home charging ended with slightly lower battery, but over 60% of away-from
home charging events were completed with greater than 90% Battery. Furthermore, looking
at the average amount of the charging consumption. The study by (Morrissey, Weldon, &
O'Mahony, 2016) indicate similar charge consumption requirements for standard chargers
and fast chargers amongst users. It would be expected that fast chargers would have both
lower duration and higher consumption charge events in allocations; however, the mean
recorded for fast chargers of 7.27kWh was similar to the mean recorded for standard
chargers of 6.93kWh at public charging points in car parks. In addition, (Morrissey, Weldon,
& O'Mahony, 2016) indicate that each within different locations of standard chargers the
mean charge consumption values recorded is on average the same with values ranging from
5.14kWh for petrol station located chargers to 6.93 kWh for car park located chargers. This
would indicate users tend to require similar amounts of energy regardless of the charge
location. Research by (Franke & Krems, 2013) adds that EV drivers actively monitor the
battery levels and base their charging decisions upon this information . Suggested is that the
amount of energy that is added per transaction of drivers with routine charging behaviour is
smaller than EV drivers that actively monitor their battery levels. Extracting a larger amount
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of energy from the grid would bring more potential for electricity demand problems. An
actively monitoring EV driver would bring more possible problems to the grid because of
their preference to let the charging consumption values to be near to the battery capacity of
the EV.
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3 | METHODOLOGY & DATA ANALYSIS
This chapter covers the proposed model, introducing the dependent variable and
independent variables, followed by the description of the data used, its preparation for
analysis. The main purpose of this paper is to determine which and how the discovered
dimensions constitute plug-in electric vehicle charging behaviour the Netherlands. In order
to test the effect of these factors affecting the charging behaviour, data analysis will be
conducted. Gaining insight in the main factors that constitute plug-in electric vehicle
charging behaviour will help the transition towards a more sustainable mobility system. The
research starts with analysing all the transactions in the database by taking the different
dimensions found in the literature into account. Thereafter, possible relationships between
the dimensions are discussed.
3.1 MODEL SETUP
After the
After the
After the
After the
After the
After the
Figure 2: Conceptual model
Time
Infrastructure
Charging
Consumption
Charging
Frequency
Charging
Duration
Location
Plug-In Electric
Vehicle
Charging
Behaviour
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3.1.2 DATA DESCRIPTION & PREPARATION
In the literature review, key factors that determine the behaviour of EV drivers have been
identified. Further research is necessary to see how these dimensions affect the charging
behaviour in the Netherlands. For analysing the different dimensions, the charging
transaction dataset is used. When it comes to data analysis, statistical techniques are
required for quantitative data. R will be the main program for analysing the data sets. This
software is able to handle large amounts of data and is able to produce descriptive and
analytical statistics in quantified data. The database that will be used contains usage-
information of the charging stations to gain a complete picture of the usage factors linked to
public infrastructure. The ability to use a quantitative dataset enables to create more reliable
results. The data set is from EvnetNL. It contains charging sessions for all charging
infrastructure maintained by EvnetNL within the Netherlands for the year 2012 until the
beginning of 2017. However, only the year 2016 is used in this research because older data
could create unreliable results. The data set includes information about the unique
transaction code, unique charge point ID, type of connector, type of start card, start and
end time of session, meter start and meter end of each transaction, as well as the total time
wherein energy transfer took place. The information sums up to 547311 charging sessions,
for 1765 charging stations and 49847 unique ID cards in 2016.
Before the data could be used for analysis, the provided databases of raw data had to be
prepared for analysing by fixing and deleting errors. During the data cleaning stage
numerous outliers were removed from the dataset. This mainly included rows that were not
complete in information or contained invalid data. Sessions that had a very low volume or a
volume of 0 kWh, a total of 140, were removed. Furthermore, volume that exceeded the
battery capacity of the largest EV in 2016, is also checked. To account for extreme outliers
and irrelevant data, durations below 1 minute and above 1000 minutes were also removed
from the data set. Combined, these filters deleted a total of 976 transactions, which is only
0.2% of the original amount of charging transactions.
22
Filter Explanation
Find and remove rows with missing data Cleaning out missing values will make sure that the dataset is clean, and ready to analyse by adding calculated fields
Remove rows with LOW Total Energy or 0 (below 0.1 kWh)
Charging 0 kWh is no charging transaction and considered an error and very small amounts of energy result in outliers and are not considered relevant (140 in total)
Remove rows with HIGH volume energy (above 95 kWh)
The largest EV car battery is 85 kWh and a 90% efficiency make transactions larger than 95 kWh errors (no errors found)
Remove rows with short charging time (below 1 minute)
Charging sessions with a time span of 1 minute are not considered relevant (In total 390)
Remove rows with long charging time (above 1000 minutes)
Charging sessions with a time span of 1000 minutes or more are considered as outliners (in total 446)
Table 1: Data filters
After data cleaning, 976 sessions, over 98.8%, remained. Following the initial data cleaning
stage, the data is prepared to help address the research questions. This mainly consisted of
adding variables based on computing.
The main data characteristics of which the charging transaction dataset that are used in the
research are shown below:
ID [StartCard] - The RFID card used at the start of transaction. The card ID is used as a unique
identifier to differentiate between users. Assumed is that every cart ID is connected to one
EV.
Charging Station [ChargepointId] - The unique code of a charging station. With this variable
it is possible to identify unique charging stations. There are 1765 charging stations in total.
Charging Station Connector[Connector] Many charging stations have two connections (two
sockets for charge plugs) and this indicates what connector was used for the transactions. In
total there are 2880 connectors.
23
Start/End Time[UTCTransactionStart / UTCTransactionStop] - The moment the transaction
was started and the plug was disconnected and the transaction was stopped (logged in UTC
time zone).
Duration[ConnectedTime] - Time difference between the start and end of a transaction. It
indicates the total time that the user was plugged in to the charging station for the given
session. The total duration is the Charge Time + Idle Time.
Charge Time[ChargeTime] - Total time wherein energy transfer took place in the charging
session. It shows the duration at which the vehicle was actively charging.
Idle Time[IdleTime] – Total time wherein no energy transfer took place in the charging
session. It shows the duration at which the vehicle was not charging during the transaction.
Total Energy Transfer[TotalEnergy] – Total amount of energy that has been transferred for
the given session, in kWh.
Max Power[MaxPower] - The actual charging speed is determined by taking the highest
possible charging speed (kW) for the session, as determined by the maximum thresholds of
both the EV and the charging station.
Location[Lat / Long] – Latitude and longitude refer to the location of a charging station. This
information was received from the Google Maps API, which allows for geocoding of postal
codes.
Postal code[Zipcode] – The location of the charging station by postal code within The
Netherlands.
Charging Station Density[csd] - A kernel density estimation is used to determine a density
function for the location of a charging station with regards to neighbouring charging stations
(Rosenblatt, 1956)
Region code[Regioncode] - Region code is the two-digit postal code, that splits the
Netherlands into various larger regions. Using this variable allows for regional distinctions
that are otherwise difficult to achieve due to the lack of granularity in the geographic
distribution of the charging stations in the data. There are 89 distinct regions, registered
within the charging session data.
24
Year[Year] – The year in which the transaction took place. All used transactions took place in
the year 2016.
Week[Week] – The week number in which the transaction session started. Week 1 starts on
the 4/01/2016, the previous week is referred to as week = 0
Day[Day] – This variable indicates on which day of the year the transaction was initiated,
from the first day of the year until the last day of the year, where 1 represents the first day.
Day of the Week[DayWeek] – The day of the week where the charging session was started,
from Monday to Sunday, where 1 represents Monday.
CONFIDENTIALITY
The charging transaction dataset contains privacy sensitive information together with
marketable sensitive information for commercial organizations. This is for the reason that
the dataset gives the opportunity to identify which EV driver was charging at given charging
points in time together with additional information about that transaction. Furthermore, it is
possible to analyse business results. Therefore, in order to allow for ethical and confidential
data analysis, confidentiality agreements had to be reached prior to sharing the requested
data. The agreements that have been agreed upon and that will be respected in the rest of
this report are:
-The data requester agrees to store the received data from EVnetNL in a secure environment
with access limited to the individuals whom are directly involved in the research project.
-The data may be only the used for the purposes of answering the research questions and/or
hypotheses upon agreed with both parties.
-The data requester agrees to not use the data for any commercial purposes.
-The requester agrees not to share or to sell the data with any other individuals or
organisations.
25
4 | RESULTS
The results are structured as follows, the research starts with a general analysis of how the
dimensions discovered in the literature have an effect on charging behaviour and charging
demand in the Netherlands. These include time, location, infrastructure, charging duration,
charging frequency, and charge consumption. The results chapter will close off with
discussing the relationships between the dimensions.
4.1 TIME
The objective is to determine how the start time and end time of a transaction has an
influence on the total number of transaction in a given time period to see what the charging
behaviour looks like. When analysing the time of day on which charging transactions take
place, a difference is made between transactions per week number, day of the week and 24
hour period of working days (Monday-Friday) and weekends, as one could expect regular
patterns to be more visible on working days. In the weekends, the EV could be used more
often on irregular leisure trips and unexpected impulsive trips, which would show a less
strict pattern. The number of all charging transactions in these two categories are shown in
hours of the day.
Figure 2: Daily charging start & stop hours (working days)
26
The workday figure 2 shows results that meet the expectations from literature. As the day
starts, a peak of transaction around 7:00 and 8:00 occurs, which could represent people
arriving at work or place of destination and plugging in their car again for a daytime charging
transaction. Then at the end of the day, the same pattern emerges: at 18:00 a transaction
start peak which could represent people arriving home and at 17:00 a transaction end peak
which could represent people leaving work. Transaction activity at night is low.
Figure 3: Daily charging start & stop hours (weekend)
The weekend figure 3 visibly demonstrates a more spread out pattern. The start of
transactions in the weekend is shaped as a parabola, with a peak at 13:00. In addition, the
end of transactions in the weekend shows a more fluctuated pattern. Overall, several
insights can be derived from the figure. First, it is probable that a substantial share of public
charging stations is used in the afternoon, as the 12:00 charging transaction start peak is not
followed by an end-peak up until 16:00. This would bring the flexibility necessary for smart
charging technology application. because of people’s different patterns of activities on the
weekends, number of transactions is distributed differently than during weekday which is in
line with the research of (Kelly, Macdonald, & Keoleian, 2012).
27
In addition, the in the literature described peak demand problems appear to be a risk, as
patterns occur in which all EV drivers are plugged in at the same time. As a consequence,
these patterns show clear peaks which can have a detrimental effect on the energy grid.
This risk seems to be smaller in the weekend because the charging transactions are more
spread out.
Figure 4: Charging sessions per week
When analysing the amount of charging sessions per week in figure 4, a clear drop could be
seen between week 28 and 34. This drop can be explained by the summer vacation within
that period which results in less EV drivers on the road in The Netherlands. The same counts
for the drop in week 17 which is a vacation period. In the months in the summer, the
amount of charging transactions is significantly lower than during the other seasons. This
could be explained by two possible reasons. First, more people will go on a holiday during
the summer months resulting in less EV drivers on the roads. Second, in warm weather, the
battery of the EV car will last longer compared to when the battery has to operate in cold
weather which results in a lower amount of charging transactions.
28
Figure 5: Charging sessions per day of the week
When looking at the charging sessions per day of the week in figure 5 it can be seen that
from Tuesday until Saturday the amount of transactions is relatively the same. However,
there is a sharp decline at Sunday which can be clarified by that on this particular day many
business are closed and people see it as a resting day which results in more people staying
home and thus lowers the amount of total transactions. Furthermore, on Monday there is a
slightly lower amount of transactions. Interesting to see is that the amount of transactions is
still high on Saturday. However, the results in section 4.4 will show that the utilization rate is
significantly lower compared to working days. This means that while the amount of
transactions stay the same, the average charge time on Saturday decreases probably
because of the EV drivers different activities.
29
4.2 CHARGING DURATION
This section shows the EV drivers charging behaviour taking the average charging duration
and the charging time in in percentage of total connected time into account. Below, figure 6
is provided in which the duration of all charging transactions is shown with the amount of
minutes the transaction lasts. Several findings can be observed.
Figure 6: Charging duration in minutes
Within figure 6, three peaks can be identified around approximately 90 minutes (1:30 hours),
530 minutes (8:50 hours) and 820 minutes (13:40 hours). The first peak, which starts around
0 minutes and ends around 200 minutes represents the largest transaction peak. This peak
will mostly represent car charging for which EV drivers have limited time available. The
second peak is located at 530 minutes and starts around 500 minutes and ends around 560
minutes. This peak can be explained by EV drivers that charge their car at work as a working
day is on average 8 hours. Finally, the last peak around 820 minutes could represent EV
drivers that that charge their car when they come home from work (18:00) and unplug their
30
EV when they leave home the next day (7:30). The research of (Morrissey, Weldon, &
O'Mahony, 2016) supports this notice by stating that charge durations were found to be
highest at household locations. These findings meet the results from the time analysis as
there is a peak in the morning for the start/ending of transactions as well as a peak for
start/ending transactions when returning from work. It can be confirmed that a share of
public charging points is being used at night and that the average time of a night transaction
takes about 820 minutes. Furthermore, a larger share of the transactions is during office
hours.
Below, in figure 7, the charging time in percentage of the total connected time is calculated.
This gives an overview in how effective the public charging points are used by the EV drivers.
The charging time is ratio is calculated by dividing the charge time by the total connected
time.
When analysing the charging time ratio, a mean of 63.22% is calculated which means that
during an transaction the actual charging time is on average only 63.22% of the total
connected time of an EV. As a consequence, a little more than one third of the total time is
an EV connected when it is not required to. This behaviour shows the massive possible gain
in making transactions more flexible and making more efficient use of the charging stations.
Charge Time Ratio
Min. 0.00
1st Qu. 29.00
Median 67.00
Mean 63.22
3rd Qu. 100.00
Max. 100.00
Table 2: descriptive statistics charge time ratio
Figure 7: Charging time ratio
31
When taking the problem of the availability of the public charging points into account. It is
desired to stimulate EV drivers to unplug their EV when the desired energy transfer has been
reached. If charging durations are better customised to the required duration, existing
charging infrastructure could be used much more efficiently. The charge infrastructure has a
lot of room for improvement in the case of effective usage. This means that instead of
focussing on expanding the network it is better to focus efforts on reducing the idle time of
charge stations. Although a large portion of this idle time is concentrated during the night,
and is thus less relevant, a significant portion of daytime connection time is not used for
charging.
4.3 CHARGING FREQUENCY
The charging frequency has also influence in economic, social and environmental
development of EV’s. A higher charging frequency may allow a reduction of EV battery size
and weight.
Due to the fact that all the charging points in the database are public charging points, it is
not possible to take the home charging transactions into account. The absence of home
charging points may reduce the average charging transactions drastically per EV driver. This
would as a result not represent the actual average charging frequency of EV drivers.
Therefore, EV drivers that charge their EV on average once a week at a public charging point
of EVnetNL will be analysed separately in addition to all the drivers in the database. This will
help to exclude EV drivers that only charge at their home charging point.
Transactions per Week (all users)
Min. 0.019
1st Qu. 0.019
Median 0.058
Mean 0.208
3rd Qu. 0.135
Max. 13.00
Table 3: Transactions per week (all users)
32
31% of all unique EV drivers in the database use the charging points of EVnetNL only once a
year. This results in a very low average per week of respectively 0.211 transactions per week
according to table 3. In addition, a number of 63% has at most only 5 transactions per year.
Which seems to be less than findings from the literature, which suggest that EV drivers
would charge their EV more often at public charging points. it is probable that a large part of
EV drivers has never charged at a public charging point, or has charged there only a few
times.
Table 4 and figure 8 take only the users into account who charge their EV at least once a
week on average. The weekly average charge events recorded in the database is 2.24
transactions per unique start card as listed in table 4. Figure 8 shows the distribution of the
charging events per week, where a wide variation between 1 and 13 charging events per
week can be seen. This could be due to the different users behaviour, from EV drivers that
only charge their EV’s when it is absolutely necessary, through users who charge their EV’s
every night and users who charge their EV whenever possible. A high count value is seen at 1
and 2 times a week, with a strong decline when frequency gets higher. Taken all the users
into account, the decline is even stronger. This is probably due to including the EV drivers
that only charge at home.
Transactions per Week (users charge atleast once a week on average)
Min. 1.00
1st Qu. 1.00
Median 2.00
Mean 2.24
3rd Qu. 3.00
Max. 13.00
Table 4: Descriptive statistics transactions per week (high frequency users)
Figure 8: Charging frequency
33
The main insights that can be derived from this analysis is that a very large amount of EV
drivers almost never charges at public charging points as 31% charged only once and 63%
charged only 5 times at most per year.
4.4 CHARGE CONSUMPTION
The frequency of EV charging transactions concerns how often an EV is charged. Below,
figure 9 shows the count of transactions related to its charge consumption per kWh. In
addition, table 5 shows the summary statistics for the charge consumption. Nearly all
transactions fall within the 0-14 kWh range and therefore a slightly larger range of 0-20 kWh
is chosen for the figure below to get a clear overview.
From figure 9 can be seen that there are two strong peaks at 7 and 9 kWh and one smaller
peak at 12 kWh. The mean of the average power consumption is 7.89 and 75% of all
transactions stay below 9 kWh as listed in table 5. According to the discussed literature, for
one quarter of the charging events, the battery of the EV had been fully or nearly fully
depleted by the time the drivers plugged in their vehicles. The majority of the EVs are PHEV’s
in the Netherlands of which it is relatively easy to empty the battery. it is likely that PHEV’s
regularly charge from an empty battery to a full one. This should lead to many PHEV’s
Charge Consumption (kWh)
Min. 0.00
1st Qu. 4.00
Median 7.00
Mean 7.89
3rd Qu. 9.00
Max. 90.00
Table 5: Descriptive statistics charge consumption
Figure 9: Charge consumption
34
charging comparable amounts of energy, explaining the peaks. The figure below shows the
charge consumption related to the EVs battery capacity. The battery capacity of an EV is
calculated by taking the maximum charge consumption in the database of an EV. Only EVs
that charge their battery at least 50 times a year are taken into this figure to create more
reliable results.
Several insights from the above figure 10 and table 6 can be derived. It can be seen that
there is a large peak at 94% peak which could be the PHEV’s that often charge close to their
battery capacity. In addition, 25% of all EVs charge at least 92% of their battery capacity
which means that a quarter of all EV drivers often charges from an empty battery to a full
one. Besides the two peaks, the figure shows a very small climb between 0% and 80% which
is comparable to the results of (Smart & Powell, 2013). Their research indicated that around
a quarter of all transactions consisted of charging an empty battery to a full one and that the
remaining transactions were evenly distributed. The main insight that can be derived is that
charge consumption in percentage of total battery capacity is evenly spread, except for the
batteries that have run for a large part or completely out of power. The line going upward at
100% percent can be explained by the fact that the calculation is based on taking the
maximum charge consumption. Every unique EV has at least one 100% consumption
transaction in percentage of total battery capacity.
Charge Consumption (kWh)
Min. 0.00
1st Qu. 50.00
Median 77.00
Mean 67.9
3rd Qu. 90.00
Max. 100.00
Table 6: Descriptive statistics charge consumption
Figure 10: Charge consumption/ capacity ratio
35
4.5 INFRASTRUCTURE
When looking at the charging behaviour by the infrastructure of the public charging
environment, it was not possible to extract the exact power output per charging point.
Therefore, the maximum power outputs seen in the database of the charging points within a
year have been taken into account to create reliable results. These outputs go from 1KW
until 23KW. It was not possible to analyse the fast chargers as they were not included in the
database and are still limited. The table below shows an analysis per given power output.
Max Power output (kW)
Average Power output (kWh)
Number of Charging points
Percentage Transactions Percentage Average active charge time (minutes)
Average energy transfer (kWh)
1 0.198 1 0,1% 381 0,1% 124 0
2 / / / / / / /
3 2.328 16 0,9% 429 0,1% 263 7
4 3.298 104 5,9% 17456 2,7% 148 7
5 3.429 8 0,5% 3970 0,6% 161 8
6 3.631 15 0,9% 540 0,1% 145 7
7 3.440 4 0,2% 1544 0,2% 172 8
8 3.112 9 0,5% 1626 0,3% 165 7
9 3.615 56 3,2% 111416 17,2% 154 8
10 3.549 104 5,9% 17091 2,6% 145 7
11 3.768 762 43,5% 241317 37,3% 142 8
12 3.968 154 8,8% 50688 7,8% 143 8
13 3.865 192 11,0% 63499 9,8% 141 8
14 4.081 266 15,2% 105867 16,4% 130 8
15 4.337 18 1,0% 8033 1,2% 144 10
16 3.545 8 0,5% 4932 0,8% 129 7
17 3.603 5 0,3% 2349 0,4% 141 7
18 3.906 11 0,6% 7895 1,2% 143 8
19 3.814 6 0,3% 902 0,1% 134 7
20 4.103 4 0,2% 1208 0,2% 137 8
21 3.781 3 0,2% 1429 0,2% 145 8
22 4.660 4 0,2% 2433 0,4% 150 12
23 4.063 2 0,1% 1103 0,2% 138 8 Table 7: Analysis per given power output
36
The first observation is that the most common power output is 11KW with also the most
transactions per year. However, the average power output seems to be quite low. Since the
majority of the EV’s in the Netherlands is an PHEV with a slow charging speed (3-4KW) the
public charging infrastructure is not being used efficient in terms of charging speed.
Improvement from the car manufacturers in improving charging speed of their EVs will
significantly change and improve the use of public charging infrastructure. It would be
expected that faster chargers would have both lower duration and higher consumption
charge events in allocations however, the mean recorded for the faster chargers was similar
to the mean recorded for slower chargers at public charging points.
4.6 LOCATION
This section takes the charging station density into account and makes a comparison
between different regions in the Netherlands to see if this has an effect on charging
behaviour and demand. Furthermore, utilization levels are calculated for different regions.
After that, clustering between the charging station network is discussed.
A kernel density estimation is used to determine a density function for the location of a
charging station with regards to neighbouring charging stations. A charging station density
(CSD) of 0.001 means a low density and a CSD of 0.008 a relatively high density. The station
density can have effects on the frequency of usage because the more stations are available
the more likely it is that they are in a suitable location for the individual driver.
37
CSD Transactions Charging points Transactions per
Charging point
Pearson
correlation*
0.001 173170 677 256
0.002 63122 182 347
0.003 30775 117 263
0.004 36490 126 290
0.005 45898 166 276
0.006 39960 115 347
0.007 95992 249 386
0.008 60681 167 363
ALL 0.799, Sig. 0.000
Table 8: Charging station density
*Pearson correlation between CSD and Transactions per Charging Point
There seems to be a relation between charging point density and the amount of transactions
as the CSD upward from 0.06 is higher than below 0.06. However, an exception can be seen
for a CSD of 0.02 because the transactions per charging point are close to the values of the
higher CSD’s points. The relation between charging station density and the transactions per
charging station resulted in a fairly large positive correlation of 0.799 as shown in the above
table 8. The view that EV drivers in total charge more or less frequently, depending on the
density of charging points in the area is noticeable in the dataset.
Comparison regions
The Netherlands can be divided in 9 zip code areas, that splits the Netherlands into various
larger regions. The table below shows the 9 different zip code areas with several statistics
about the amount of transactions. Different areas have different amount of populations and
charging points. Goal Is to identify if these different locations and populations have an effect
on frequency of usage of public infrastructure.
38
Zip code Area Transactions Charging
points
Transactions
per Charging
point
Population People per
charging
point
Transactions per
person
1000-1999 97013 240 404 2638670 10994 0,037
2000-2999 119829 291 412 2494910 8574 0,048
3000-3999 113674 275 413 2874305 10452 0,040
4000-4999 37005 150 247 1328675 8858 0,028
5000-5999 56656 241 235 2096650 8700 0,027
6000-6999 44251 206 215 1824660 8858 0,024
7000-7999 27343 136 201 1658980 12198 0,016
8000-8999 21227 101 210 1017420 10073 0,021
9000-9999 13139 84 156 1043265 12420 0,013
Table 9: Zip code area
When looking at the transactions per charging point in
table 9, the following results show. The three zip code
areas with the highest population have on average the
most transactions per charging point while there at not
significant more people per charging point. These three
zip code areas represent the “Randstad” which is a
densely populated area in the Netherlands with over 7
million inhabitants. It consists of a cluster of the four
biggest cities of the country (Amsterdam, Rotterdam,
The Hague and Utrecht) as well as several smaller
cities, towns and urbanized villages. The zip code areas
in the urban region show a much smaller amount of
transactions per person while the people per charging
point is on average higher.
Figure 11: Zip code areas in the Netherlands
39
Utilization levels per region
Figure 12 shows the variation in utilization levels across the data set for three different zip
code areas per day of the week.
The three regions, in 2016, had a fairly low utilization level. Important to notice is the
difference between the zip code areas. The 2000-2999 area which represents the
“Randstad” in the Netherlands has clearly a higher utilization level, hovering around 20%.
The other two zip code areas stick around 7 and 10%. There is a significant difference
between the different regions in the Netherlands. Also, a clear drop can be seen during the
weekends, especially on Sunday for all regions. Also noteworthy, is the seasonality in weekly
average utilization levels in figure 13. A clear drop can be seen within the summer period for
regions 2000-2999 and 5000-5999. However, region 9000-9999 does not show this drop.
Possible explanation could be the large agricultural sector in the Northern part of the
Netherlands. Working in this sector makes it more difficult to go on vacation for a long time
and will result in more people staying in the area.
Figure 12: Utilization levels per day of the week
Figure 13: Utilization levels per week number
40
Clustering within the charging station network
This section will shed light on the answer on how many EV drivers charging stations have in
common with each other and tells if there is any clustering between charging stations. A
charging point forms a cluster if it has the same unique user as other charging points. The
charging station network of EVnetNL exists out of 1762 charging stations with over 49817
unique users in The Netherlands. A network consists out of nodes which are connected to
each other by an edge if they have the same user (figure 14). In this case, the nodes are the
charging stations and edges are the unique users. Goal is to investigate how the charging
stations and unique users are related to each other in terms of clustering.
Degree centrality
Average degree centrality of al charging stations corresponds to the fraction of all charging
stations in the network to which charging station is connected by having the same unique
user.
𝑫𝒆𝒈𝒓𝒆𝒆 𝒄𝒆𝒏𝒕𝒓𝒂𝒍𝒊𝒕𝒚(𝒊) =𝒅𝒆𝒈𝒓𝒆𝒆 𝒊
𝒏 − 𝟏
Where i represents the charging station and n the total number of charging stations.
Clustering coefficient
a clustering coefficient is a measure of the degree to which the charging stations in a graph
tend to cluster together by having the same users.
𝒄𝒍𝒖𝒔𝒕𝒆𝒓𝒊𝒏𝒈 𝒄𝒐𝒆𝒇𝒇𝒊𝒄𝒊𝒆𝒏𝒕(𝒊) = 𝒍𝒊𝒏𝒌𝒔 𝒂𝒎𝒐𝒏𝒈 𝒏𝒆𝒊𝒈𝒉𝒃𝒐𝒓𝒔 𝒐𝒇 𝒊
𝒑𝒐𝒔𝒔𝒊𝒃𝒍𝒆 𝒍𝒊𝒏𝒌𝒔 𝒂𝒎𝒐𝒏𝒈 𝒏𝒆𝒊𝒈𝒉𝒃𝒐𝒓𝒔 𝒐𝒇𝒊
Average degree centrality 176.54
Maximum degree centrality 733
Minimum degree centrality 9
Clustering coefficient 0.30
Table 10: Descriptive statistics charging station network
41
According to table 10, the degree centrality is 176.54 which means that a charging station
has on average 176.54 the same users with another charging station. Furthermore, the
maximum degree centrality is 733 and the lowest is 9 in the network. This huge difference
could be explained by the difference in public and semi-public charging infrastructure as
public charging points are accessible and usable for anyone. While, semi-public charging
stations are on private domain and thus have less unique users. Still, there remains a
significant difference in the number of having the same users which has an effect on the
utilization rate. The average clustering coefficient is 0.30 which means that there is some
degree of clustering in the network of charging stations. This indicates that there groups of
large amount of EV drivers that often charge within a certain area. The figure below shows a
visualisation of a network with a clustering coefficient of 0.30 to give an idea how the
network of charging stations look like.
Figure 14: Network visualisation
42
4.7 RELATIONSHIPS BETWEEN DIMENSIONS
Several relations between these have become apparent and deserve further explanation in
this research, as these relations complement the insights in how the factors influence
charging behaviour. These relations will be stated and explored below.
Charging duration – charge consumption
The charging duration in relationship to the amount of energy transferred during a charging
session is much discussed (Franke & Krems, 2013). A significant correlation is found between
the charging duration and the amount of energy transferred as can be seen in table 9. A
correlation of 0.222 suggests that for a large share, the duration has little correlation with
the amount of energy transferred. This indicates that public charging points are often
occupied often longer than would be needed. In this situation, a correlation of 1 would be
optimal with regard to the charging point availability
problem. As this means that an EV is plugged in exactly the
time required for charging the EV.
Charging frequency – charge consumption.
According to research of (Smart & Powell, 2013) it is stated that as the charging frequency of
an EV driver is higher, the amount of energy transferred would be lower as the transaction
starts with a less empty battery. Transactions with a lower amount of energy could be
favourable for relieving the energy grid demand peaks. However, a small correlation of 0.002
has been found as shown in table 10 which implies that
this relationship is not in expectation with earlier
research.
Charging duration –charge
consumption
Pearson Correlation 0.222
Sig. 0.000
Table 11: Correlation statistics for charging duration - charge consumption
Charging frequency –charge
consumption
Pearson Correlation 0.002
Sig. 0.137 Table 12: Correlation statistics for charging frequency - charge consumption
43
Charging point density – charging frequency
The research of (Kelly, Macdonald, & Keoleian, 2012) express that as charging point density
increases, range anxiety decreases, as there is always a charging point nearby. EV drivers
could charge more frequent due to the plentiful options of charging points, or they charge
less in a high charging point density area as they are self-assured that there is always a
charging point nearby. The analysis showed a small negligible correlation of -0.072 which can
be seen in table 13. The above notion is not visible in the dataset. Possible reason is that the
Netherlands is a relatively small and densely populated country. For that reason, even in less
populated regions, there always is a charging point accessible. However, as in the earlier
discussed location analysis. The total number of transactions for each charging point does
increase significantly when there is a high charging point density. It seems that EV drivers
from outside the region often charge in high charging station density regions because many
of them work there as there are the most employment
opportunities in this regions. This explains why a high
charging station density increases the total number of
transactions in the region and not the individual charging
frequency.
Charging frequency –charge
consumption
Pearson Correlation -0.072
Sig. 0.000
Table 13: Correlation statistics for charging frequency - charge consumption
44
5 | CONCLUSION
The main goal of this research was to develop a better understanding into the charging
behaviour in the Netherlands. The electric vehicle (EV) user is with his charging behaviour an
important parameter in a well-functioning charging system. This research aimed at
understanding what this charging behaviour looks like and what factors constitute this
behaviour, which may help to develop strategies for promoting a more efficient utilization of
the charging. Understanding the charging behaviour and demand of EV users helps to
optimize and evenly distribute the utilization rate. This leaded to the following main
research question:
What are the main factors that constitute plug-in electric vehicle charging behaviour
and how are they formed in the Netherlands?
Past literature was used to identify the main factors. These main factors are time, duration,
frequency, consumption, infrastructure and location. With use of data analysis and using
statistical techniques, the Dutch public infrastructure and charging behaviour could be
confronted with these dimensions.
5.1 THEORETICAL CONTRIBUTION
This research contributes in several ways to the current literature. The first contribution is
the inclusion of the Dutch public charging points and its users. There are several studies that
examine charging behaviour, but most of them only include studies done in other countries.
This is one of the few studies that has as a setting the Dutch public infrastructure.
The second contribution is the more modern setting of this research. Previous research on
charging behaviour was during a period where the use of EVs was fairly new and on the rise.
Most research papers included are from a period a few years earlier. This research however
is during a period where EVs become more mainstream and the pressure to stimulate EVs
increases. Also, the public charging infrastructure in the Netherlands is nowadays much
better than a few years ago, which can change the behaviour of EV drivers and its effects on
the demand of EV users.
45
Following on from the preview paragraph, the continuing expanding and modernising
network of charging points has resulted in some interesting results.
With regard to the time of charging, a difference can be seen between working days and the
weekend. Two clear peaks of starting and stopping charging transactions are visible which
indicate that many EV drivers have a similar charging routine. The weekend shows a
different pattern.
Taking charging duration into consideration. Three large peaks are identified around
approximately 90 minutes (1:30 hours), 530 minutes (8:50 hours) and 820 minutes (13:40
hours which tells that these are the most common charge durations. The actual charging
time is on average only 63.22% of the total connected time of an EV. As a consequence, a
little more than one third of the total time is an EV connected when it is not required to. This
behaviour shows the massive possible gain in making transactions more flexible and making
more efficient use of the charging stations.
Results on charging frequency show that EV drivers that are dependent on public charging,
charge on average 2,24 times a week. Taking all EV drivers into consideration, a very large
amount of EV drivers almost never charges at public charging points as 31% charged only
once and 63% charged only 5 times at most per year. EV drivers that are dependent on
public charging infrastructure, charge on average 2.24 times a week.
When looking at the energy transfer, The main insight that can be derived is that charge
consumption in percentage of total battery capacity is evenly spread, except for the
batteries that have run for a large part or completely out of power.
The average power is reasonably the same between all the power outputs. As a
consequence, the public charging infrastructure is not being used efficient in terms of
charging speed. Improvement from the car manufacturers in improving charging speed of
their EVs will significantly change and improve the use of public charging infrastructure.
Looking to the results from the location dimension. EV drivers individual do not charge more
or less frequent in an area with a high charging station density which is contrary to (Kelly,
Macdonald, & Keoleian, 2012). However, the view that EV drivers in total charge more or
46
less frequently, depending on the density of charging points in the area is noticeable in the
dataset. It seems that EV drivers from outside the region often charge in high charging
station density regions because many of them work there as there are the most employment
opportunities in this regions. This explains why a high charging station density increases the
total number of transactions in the region and not the individual charging frequency.
5.2 MANAGERIAL IMPLICATIONS
This section describes the implications for the charging station operators as well as the
electricity producers. Charging station operators will want to minimize charging duration
while maximizing active charging duration, in order to optimize revenues. Using the findings
in this study, businesses can consider new price structures that might motivate users to
charge for shorter periods of time, and when they only need to actively charge. This would
however decrease the potential for smart-charging technology, as this will decrease the
longer charging durations required for smart-charging. Many EV drivers will still connect for
long periods during the night and during work times. This gives the opportunity to electricity
producers to influence and control the charging procedure, to reduce the energy demand
problem and still make sure that the battery of the EV is fully charged. Furthermore, many
EV drivers charge based on routine, with clear charging start and stopping peaks. To
minimize these problems, charging station operators could use the potential of smart
charging technology to make EV charging behaviour more efficient.
5.3 LIMITATIONS AND FUTURE WORK
This research was not without limitations, first of all, it was not possible to accurately distinct
PHEV and FEV and EV model in the dataset due to privacy restrictions. Making assumptions
to make a difference between a PHEV and FEV would lead to a lower reliability of the results.
A suggestion for future research would be the inclusion of a distinction between EV types.
A second limitation is that a user, identified by a unique user ID, is allowed to use the same
charging card for various vehicles. This has an impact on the quality of the research, as it
47
cannot be guaranteed that all charging sessions of a user are linked to one vehicle, or vehicle
type. However, this risk is expected to be limited as some charging card providers prohibit
the multi-use of their charging cards.
Numerous foundations have been laid out or touched upon in this research paper. Many are
a continuation of previous research while others are a re-evaluation of what has been
studied before. Future research can build upon these dimensions.
48
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50
7 | APPENDIX
7.1 LIST OF FIGURES
Figure 1: Emission of greenhouse gasses in the Netherlands (CBS, 2016).............................................. 6
Figure 2: Daily charging start & stop hours (working days) .................................................................. 25
Figure 3: Daily charging start & stop hours (weekend) ......................................................................... 26
Figure 4: Charging sessions per week ................................................................................................... 27
Figure 5: Charging sessions per day of the week .................................................................................. 28
Figure 6: Charging duration in minutes ................................................................................................. 29
Figure 7: Charging time ratio ................................................................................................................. 30
Figure 8: Charging frequency ................................................................................................................ 32
Figure 9: Charge consumption .............................................................................................................. 33
Figure 10: Charge consumption/ capacity ratio .................................................................................... 34
Figure 11: Zip code areas in the Netherlands........................................................................................ 38
Figure 12: Utilization levels per day of the week .................................................................................. 39
Figure 13: Utilization levels per week number ...................................................................................... 39
Figure 14: Network visualisation ........................................................................................................... 41
7.2 LIST OF TABLES
Table 1: Data filters ............................................................................................................................... 22
Table 2: descriptive statistics charge time ratio .................................................................................... 30
Table 3: Transactions per week (all users) ............................................................................................ 31
Table 4: Descriptive statistics transactions per week (high frequency users) ...................................... 32
Table 5: Descriptive statistics charge consumption .............................................................................. 33
Table 6: Descriptive statistics charge consumption .............................................................................. 34
Table 7: Analysis per given power output ............................................................................................. 35
Table 8: Charging station density .......................................................................................................... 37
Table 9: Zip code area ........................................................................................................................... 38
Table 10: Descriptive statistics charging station network ..................................................................... 40
Table 11: Correlation statistics for charging duration - charge consumption ....................................... 42
Table 12: Correlation statistics for charging frequency - charge consumption .................................... 42
Table 13: Correlation statistics for charging frequency - charge consumption .................................... 43
51
7.3 R CODE
library(data.table)
library(ggmap)
library(ggplot2)
library(dplyr)
library(scales)
library(ggthemes)
library(McSpatial)
library(base)
library(stringi)
library(plotly)
library(igraph)
# Transform Dataset into Data frame and Datatable format.
DF.Transactions <- data.frame(Transactions)
DT.Transactions <- data.table(DF.Transactions)
#Add year number
DF.Transactions$Year <- strftime(DF.Transactions$UTCTransactionStart,format="%Y")
# Only use data from the year 2016
DF.Transactions <- DF.Transactions[(DF.Transactions$Year == "2016"), ]
# Remove colums with LOW Total Energy or 0
DF.Transactions <- DF.Transactions[-which(DF.Transactions$TotalEnergy < 0.101),]
#Find and remove rows with missing data.
DF.Transactions <- subset(DF.Transactions, select = -c(index) )
DF.Transactions <- DF.Transactions[complete.cases(DF.Transactions), ]
# Remove rows with HIGH volume
DF.Transactions <- DF.Transactions[-which(DF.Transactions$TotalEnergy > 95), ]
#remove rows with Low time
DF.Transactions <- DF.Transactions[-which(DF.Transactions$ChargeTime < 0.016), ] #Below 1
minute
#remove rows with HIGH time
DF.Transactions <- DF.Transactions[-which(DF.Transactions$ChargeTime > 16.7), ] #Above 1000
minutes
#Add week number
DF.Transactions$week <- strftime(DF.Transactions$UTCTransactionStart,format="%W")
DF.Transactions$HourPerDay <- strftime(DF.Transactions$UTCTransactionStart,format="%j:%H")
#Add day number
DF.Transactions$Day <- strftime(DF.Transactions$UTCTransactionStart,format="%j")
DF.Transactions$Day <- as.numeric(DF.Transactions$Day)
#Add day of the week number
DF.Transactions$DayWeek <- strftime(DF.Transactions$UTCTransactionStart,format="%u")
DF.Transactions$DayWeek <- as.numeric(DF.Transactions$DayWeek)
#Convert latitude/longitude to location
latlon <- unique(DF.Transactions[,c('lat','lon')])
latlon$textAddress <- mapply(FUN = function(lon, lat) revgeocode(c(lon, lat)), latlon$lon,
latlon$lat)
#Calculate charging point density
kdens <- kdensity(latlon$lon,latlon$lat, kilometer=TRUE, noplot=FALSE,
dmin=0, dmax=0, dlength=nrow(latlon), h=c(0.1,0.1), kern="gaussian",
nsamp=0,
confint=TRUE, pval=.05)
latlon$csd <- kdens$dhat
latlon$Zipcode <- stri_extract_last_regex(latlon$textAddress, "\\d{4}")
latlon$Regioncode <- substr(latlon$Zipcode, 0, 2)
DF.Transactions <- merge(DF.Transactions, latlon, by = c("lon", "lat"))
DF.Transactions$KernalDensity <- ifelse((DF.Transactions$csd) > 0.007, 0.008,
ifelse((DF.Transactions$csd) > 0.006, 0.007,
ifelse((DF.Transactions$csd) > 0.005, 0.006,
52
ifelse((DF.Transactions$csd) > 0.004,
0.005,
ifelse((DF.Transactions$csd) >
0.003, 0.004,
ifelse((DF.Transactions$csd) > 0.002, 0.003,
ifelse(DF.Transactions$csd > 0.001, 0.002, 0.001)))))))
#Count transactions per charging station density area
DF.Transactions$count <- as.numeric(ave(DF.Transactions$KernalDensity,
DF.Transactions$KernalDensity, FUN = length))
RegionChargepoint <- unique(DF.Transactions[,c('ZipcodeArea','ChargePoint')])
RegionChargepoint$count <- as.numeric(ave(RegionChargepoint$ZipcodeArea,
RegionChargepoint$ZipcodeArea, FUN = length))
# Zip code area
DF.Transactions$ZipcodeArea <- ifelse((DF.Transactions$Zipcode) > 9000, 9999,
ifelse((DF.Transactions$Zipcode) > 8000, 8999,
ifelse((DF.Transactions$Zipcode) > 7000, 7999,
ifelse((DF.Transactions$Zipcode) > 6000,
6999,
ifelse((DF.Transactions$Zipcode) >
5000, 5999,
ifelse((DF.Transactions$Zipcode) > 4000, 4999,
ifelse(DF.Transactions$Zipcode > 3000, 3999,
ifelse((DF.Transactions$Zipcode) > 2000, 2999, 1999))))))))
# Count transactions per StartCard
FreqStartcards <- as.data.frame(table(DF.Transactions$StartCard))
colnames(FreqStartcards) <- c("StartCard", "TransactionsStartcard")
DF.Transactions <- merge(FreqStartcards, DF.Transactions, by = "StartCard")
FreqStartcards$TransactionsStartcard <- FreqStartcards$TransactionsStartcard
FreqStartcards$TransactionsStartcard <- round(FreqStartcards$TransactionsStartcard)
#Round Charge consumption
DF.Transactions$TotalEnergy <- round(DF.Transactions$TotalEnergy)
#Count Frequency of each hour on each day
FreqHourPerDay <- as.data.frame(table(DF.Transactions$HourPerDay))
colnames(FreqHourPerDay) <- c("HourPerDay", "TransactionsHourPerDay")
DF.Transactions <- merge(FreqHourPerDay, DF.Transactions, by = "HourPerDay")
#Per hour Start and Stop times
HourStart <- as.POSIXlt(DF.Transactions$UTCTransactionStart)$hour
halfhour <- ifelse(as.POSIXlt(DF.Transactions$UTCTransactionStart)$min > 30, 1, 0)
DF.Transactions$HourStart <- (HourStart + halfhour)
HourStop <- as.POSIXlt(DF.Transactions$UTCTransactionStop)$hour
halfHourStop <- ifelse(as.POSIXlt(DF.Transactions$UTCTransactionStop)$min > 30, 1, 0)
DF.Transactions$HourStop <- (HourStop + halfHourStop)
# Turn Connected time from hours to minutes
DF.Transactions$ConnectedTimeMinutes <- DF.Transactions$ConnectedTime * 60
DF.Transactions$ConnectedTimeMinutes <- round(DF.Transactions$ConnectedTimeMinutes, -1)
# Actual charge time ratio
DF.Transactions$ChargeRatio <- DF.Transactions$ChargeTime / DF.Transactions$ConnectedTime
DF.Transactions$ChargeRatio <- DF.Transactions$ChargeRatio * 100
DF.Transactions$ChargeRatio <- round(DF.Transactions$ChargeRatio)
#Type of charging point (based on max charging speed KwH)
ChargePointMax <- aggregate(DF.Transactions$MaxPower, list(DF.Transactions$ChargePoint), max)
colnames(ChargePointMax) <- c("ChargePoint", "MaxPowerperPoint")
ChargePointMax$MaxPowerperPoint <- round(ChargePointMax$MaxPowerperPoint)
DF.Transactions <- merge(DF.Transactions, ChargePointMax, by = "ChargePoint")
#Max charging speed measured per Startcard
MaxPowerUser <- aggregate(DF.Transactions$MaxPower, list(DF.Transactions$StartCard), max)
colnames(MaxPowerUser) <- c("StartCard", "MaxPowerperUser")
DF.Transactions <- merge(DF.Transactions, MaxPowerUser, by = "StartCard")
#average charging speed per type of charging point
53
PointTypeAverage <- aggregate(DF.Transactions$MaxPower,
list(DF.Transactions$MaxPowerperPoint), mean)
colnames(PointTypeAverage) <- c("Output", "AverageOutput")
#Average chargetime per type of charging point
AverageTimePerOutput <- aggregate(DF.Transactions$ChargeTime,
list(DF.Transactions$MaxPowerperPoint), mean)
colnames(AverageTimePerOutput) <- c("Output", "ChargeTime")
AverageTimePerOutput$ChargeTime <- AverageTimePerOutput$ChargeTime * 60
AverageTimePerOutput$ChargeTime <- round(AverageTimePerOutput$ChargeTime)
#Chargepoint ratio
DF.Transactions$ChargePointRatio <- DF.Transactions$MaxPower / DF.Transactions$MaxPowerperUser
DF.Transactions$ChargePointRatio <- DF.Transactions$ChargePointRatio * 100
DF.Transactions$ChargePointRatio <- round(DF.Transactions$ChargePointRatio)
#Mean energy transfer per type of charging point
AverageEnergyPerTransfer <- aggregate(DF.Transactions$TotalEnergy,
list(DF.Transactions$MaxPowerperPoint), mean)
colnames(AverageEnergyPerTransfer) <- c("Output", "TotalEnergy")
AverageEnergyPerTransfer$TotalEnergy <- round(AverageEnergyPerTransfer$TotalEnergy)
#---EV: max and mean charging amount /assumption one car per card ID
ev.mean <- aggregate(DF.Transactions$TotalEnergy, list(DF.Transactions$StartCard), mean)
ev.max <- aggregate(DF.Transactions$TotalEnergy, list(DF.Transactions$StartCard), max)
ev.model <- merge(ev.mean, ev.max, by = "Group.1")
colnames(ev.model) <- c("StartCard","ev_mean","ev_max")
DF.Transactions <- merge(DF.Transactions, ev.model, by = "StartCard")
#Charge consumption / capacity ratio
DF.Transactions$ConsumptionRatio <- DF.Transactions$TotalEnergy / DF.Transactions$ev_max
DF.Transactions$ConsumptionRatio <- DF.Transactions$ConsumptionRatio * 100
DF.Transactions$ConsumptionRatio <- round(DF.Transactions$ConsumptionRatio)
#Charge consumption / capacity ratio (only users which charged atleast 50 times in a year)
DF.Transactions.new <- DF.Transactions[DF.Transactions$StartCard %in%
names(which(table(DF.Transactions$StartCard) > 50)), ]
#Utilization rate
DF.Transactions$DiffTime <- difftime(DF.Transactions$UTCTransactionStop,
DF.Transactions$UTCTransactionStart, units = "hours")
DF.Transactions$Utilization.week <- as.numeric(DF.Transactions$DiffTime/1248) * 100
DF.Transactions$Utilization.weeknumber <- as.numeric(DF.Transactions$DiffTime/168) * 100
#Utilization region per weeknumber
Utilization.region.week <- aggregate(Utilization.weeknumber ~ week + Connector + ZipcodeArea,
data = DF.Transactions, FUN = 'sum')
Utilization.region.week$Utilization.weeknumber <-
Utilization.region.week$Utilization.weeknumber/1.45
Utilization.region.week$week<- as.numeric(Utilization.region.week$week)
Utilization1.2999 <- Utilization.region.week[Utilization.region.week$ZipcodeArea == "2999", ]
Utilization1.region2999.mean <- aggregate(Utilization1.2999$Utilization.week,
list(Utilization1.2999$week), mean)
colnames(Utilization1.region2999.mean) <- c("week", "AverageUtilization2999")
Utilization1.5999 <- Utilization.region.week[Utilization.region.week$ZipcodeArea == "5999", ]
Utilization1.region5999.mean <- aggregate(Utilization1.5999$Utilization.week,
list(Utilization1.5999$week), mean)
colnames(Utilization1.region5999.mean) <- c("week", "AverageUtilization5999")
Utilization1.9999 <- Utilization.region.week[Utilization.region.week$ZipcodeArea == "9999", ]
Utilization1.region9999.mean <- aggregate(Utilization1.9999$Utilization.week,
list(Utilization1.9999$week), mean)
colnames(Utilization1.region9999.mean) <- c("week", "AverageUtilization9999")
Utilization.weeknumber.combined <- merge(Utilization1.region2999.mean,
Utilization1.region5999.mean, by = "week")
Utilization.weeknumber.combined <- merge(Utilization.weeknumber.combined,
Utilization1.region9999.mean, by = "week")
#Utilization region per day of the week
Utilization.region <- aggregate(Utilization.week ~ DayWeek + Connector + ZipcodeArea, data =
DF.Transactions, FUN = 'sum')
Utilization.region$Utilization.week <- Utilization.region$Utilization.week/1.09
Utilization.region$DayWeek <- as.numeric(Utilization.region$DayWeek)
54
Utilization.2999 <- Utilization.region[Utilization.region$ZipcodeArea == "2999", ]
Utilization.region2999.mean <- aggregate(Utilization.2999$Utilization.week,
list(Utilization.2999$DayWeek), mean)
colnames(Utilization.region2999.mean) <- c("Day", "AverageUtilization2999")
Utilization.5999 <- Utilization.region[Utilization.region$ZipcodeArea == "5999", ]
Utilization.region5999.mean <- aggregate(Utilization.5999$Utilization.week,
list(Utilization.5999$DayWeek), mean)
colnames(Utilization.region5999.mean) <- c("Day", "AverageUtilization5999")
Utilization.9999 <- Utilization.region[Utilization.region$ZipcodeArea == "9999", ]
Utilization.region9999.mean <- aggregate(Utilization.9999$Utilization.week,
list(Utilization.9999$DayWeek), mean)
colnames(Utilization.region9999.mean) <- c("Day", "AverageUtilization9999")
Utilization.dayoftheweek.combined <- merge(Utilization.region2999.mean,
Utilization.region5999.mean, by = "Day")
Utilization.dayoftheweek.combined <- merge(Utilization.dayoftheweek.combined,
Utilization.region9999.mean, by = "Day")
#Igraph
all.chargingPoints <- DT.Transactions[, list(name=unique(ChargePoint), type=TRUE)]
all.EVdrivers <- DT.Transactions[, list(name=unique(StartCard), type=FALSE)]
all.verticles <- rbind(all.chargingPoints, all.EVdrivers)
g.charging.driver <- graph.data.frame(DT.Transactions[, list(ChargePoint, StartCard)],
directed=FALSE,
vertices=all.verticles)
g.startcard <- bipartite.projection(g.charging.driver)$proj2
mean(degree(g.startcard))
transitivity(g.startcard)
diameter(g.startcard)
#Correlations
cor.test(DF.Transactions$TotalEnergy, DF.Transactions$ConnectedTime)
cor.test(DF.Transactions$TotalEnergy, DF.Transactions$TransactionsStartcard)
cor.test(DF.Transactions$KernalDensity, DF.Transactions$TransactionsStartcard)
#Data presentation
#Utilization rate
ggplot(Utilization.postcode, aes(Day)) +
geom_line(aes(y = AverageUtilization2999, colour = "2000-2999")) +
geom_line(aes(y = AverageUtilization5999, colour = "5000-5999")) +
geom_line(aes(y = AverageUtilization9999, colour = "9000-9999")) + scale_x_discrete(limits =
c(1:7)) + ggtitle("Charging Station Utilization Level Variation ") + ylim(0,40) + theme_few()
+ labs(y = "Utilization Level (%)")
ggplot(Utilization.weeknumber.combined, aes(week)) +
geom_line(aes(y = AverageUtilization2999, colour = "2000-2999")) +
geom_line(aes(y = AverageUtilization5999, colour = "5000-5999")) +
geom_line(aes(y = AverageUtilization9999, colour = "9000-9999")) + ggtitle("Charging Station
Utilization Level Variation ") + ylim(0,40) + xlim(1,51) + theme_few() + labs(y =
"Utilization Level (%)")
#Plot transactions per weeknumber
WeekNumber <- table(DF.Transactions$week)
plot(WeekNumber, type = "l", main="Charging Sessions per Week", xlab="Week", ylab="Total
Count", col= "red")
#Plot transactions per day of the week
DayOfTheWeek <- table(DF.Transactions$DayWeek)
plot(DayOfTheWeek, type = "l", main="Charging Sessions per Day of the Week", xlab="1 = Monday,
7 = Sunday", ylab="Total Count", col= "red")
#Plot transactions per hour on working days
HourlyTime <- table(DF.TransactionsWorking$Hour)
plot(HourlyTime, type = "o", main="Charging Sessions per Hour (Working days)", xlab="Hour",
ylab = "Total Count", col= "red", cex.axis=0.70)
#Plot transactions per hour in weekend
HourlyTime <- table(DF.TransactionsWeekend$Hour)
plot(HourlyTime, type = "o", main="Charging Sessions per Hour (Weekend)", xlab="Hour", ylab =
"Total Count", col= "red", cex.axis=0.70)
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#Turn dataset in only working days/weekend data
DF.TransactionsWorking <- DF.Transactions[-which(DF.Transactions$DayWeek > 5), ]
DF.TransactionsWeekend <- DF.Transactions[-which(DF.Transactions$DayWeek < 6), ]
HourlyTimeWeekend <- table(DF.Transactions$Hour=="1")
#Plot charging duration in minutes
ChargingDuration <- table(DF.Transactions$ConnectedTimeMinutes)
plot(ChargingDuration, type = "l", main="Charging Duration", xlab="Charging Duration
(minutes)", ylab = "Total Count (transactions per 10 mintutes)", col= "red", cex.axis=0.8,
xlim=c(0, 1000))
#Plot average charge consumption
ChargeConsumption <- table(DF.Transactions$TotalEnergy)
plot(ChargeConsumption, type = "l", main="Charge Consumption", xlab="Charge Consumption
(kWh)", ylab = "Total Count", col= "red", cex.axis=0.70, xlim=c(0, 20))
#Plot consumption/capacity ratio
ConsumptionRatio <- table(DF.Transactions.new$ConsumptionRatio)
plot(ConsumptionRatio, type = "l", main="Charge Consumption / Capacity Ratio", xlab="Charge
consumption in percentage of total battery capacity", ylab = "Total Count", col= "red",
cex.axis=0.70, xlim=c(6, 100), ylim=c(0,11000))
#Plot charging time ratio
ChargeRatio <- table(DF.Transactions$ChargeRatio)
plot(ChargeRatio, type = "l", main="Charging Time Ratio ", xlab="Charging time in percentage
of total connected time", ylab = "Total Count", col= "red", xlim=c(1,100), cex.axis=0.7,
ylim=c(0, 10000))
#Daily charging start $ Stop hours (Weekend)
session_count <- table(DF.TransactionsWeekend$HourStart)
plot(session_count, type = "l", main = "Daily Charging Start & Stop Hours (Weekend)",
xlab="Hour", ylab="Total Count", cex.axis=0.70) #start and end times
session_count <- data.frame(table(DF.TransactionsWeekend$HourStop))
lines(session_count$Freq, col = "red"); rm(session_count)
legend("topleft", inset=.05, c("Start Time","End Time"), lwd=2, lty=c(1, 1, 1, 1, 2),
col=c("black","red"))
#Daily charging start $ Stop hours (Working Days)
session_count <- table(DF.TransactionsWorking$HourStart)
plot(session_count, type = "l", main = "Daily Charging Start & Stop Hours (Weekend)",
xlab="Hour", ylab="Total Count", cex.axis=0.70) #start and end times
session_count <- data.frame(table(DF.TransactionsWorking$HourStop))
lines(session_count$Freq, col = "red"); rm(session_count)
legend("topleft", inset=.05, c("Start Time","End Time"), lwd=2, lty=c(1, 1, 1, 1, 2),
col=c("black","red"))
#Charging frequency per startcard(Startcard occurs atleast 52 times)
FreqStartcardsWeek <- FreqStartcards[-which(FreqStartcards$TransactionsStartcard < 260), ]
FreqStartcardsWeek$TransactionsStartcard <- FreqStartcardsWeek$TransactionsStartcard /52
FreqStartcardsWeek$TransactionsStartcard <- round(FreqStartcardsWeek$TransactionsStartcard)
CountStartCards <- table(FreqStartcards$TransactionsStartcard)
plot(CountStartCards, type = "l", main="Charging Frequency", xlab="Charging Frequency
(Transactions per Week)", ylab = "Total Count", col= "red", cex.axis=1, xlim = c(1,13))